Roller-Compacted Concrete Dams Rehabilitation in Terms of Different Problem
Bibliographic record
Abstract
Roller-Compacted Concrete (RCC) started in the US and Canada near 30 years ago, at that time it was a new method to construct gravity dams or rehabilitated. After passing these years, it’s become one of the popular methods in designing dams and called Roller Compacted Concrete. No slump is the specific property of the concrete of RCC dams. This type of dam needs place concrete in thin layers and compacted by roller to meet the require compaction. By one side, RCC dams can dissipate energy by stair step slope more than 70 percent of water energy and from the other side, all of the ordinary dams need to have an emergency spillway, but due to using all the length of the crest for spillway in the RCC dams, it is removed and the cost of constructing decreased. One of the most common problems which occur in the RCC dams at the beginning of its usage is hairline cracks throughout dam. This kind of cracks can start from upstream to the downstream. Rehabilitations have got several options depends on the kind of cracks and situation of cracks on dams such as drill holes, injecting grout, using different type of membrane and geomembrane, covered sealing system and covered geomembrane content. In this paper, try to investigate the different rehabilitation way of RCC in detailed and specified the best way for each kind of cracks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".